★smartFactory_catBoost_optuna_Yumin (1).ipynb

{
 "cells": [
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   "cell_type": "code",
   "execution_count": 4,
   "id": "hC6xTjdGA-rj",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hC6xTjdGA-rj",
    "outputId": "d8190ed1-f644-4646-bc52-0774a6befdb2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Installing collected packages: Mako, colorlog, cmaes, alembic, optuna\n",
      "Successfully installed Mako-1.2.4 alembic-1.9.4 cmaes-0.9.1 colorlog-6.7.0 optuna-3.1.0\n"
     ]
    }
   ],
   "source": [
    "!pip install catboost\n",
    "!pip install optuna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9b5f48b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Installing collected packages: pyasn1, rsa, pyasn1-modules, oauthlib, cachetools, requests-oauthlib, google-auth, tensorboard-plugin-wit, tensorboard-data-server, protobuf, markdown, grpcio, google-auth-oauthlib, absl-py, termcolor, tensorflow-io-gcs-filesystem, tensorflow-estimator, tensorboard, opt-einsum, libclang, keras, google-pasta, gast, flatbuffers, astunparse, tensorflow-intel, tensorflow\n",
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     ]
    }
   ],
   "source": [
    "!pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "94682a80",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "94682a80",
    "outputId": "0d6c6e92-9eee-4833-c6bb-8c4555d5c8e4"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import random\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt   \n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import regularizers\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Dropout, Activation\n",
    "\n",
    "from sklearn.metrics import f1_score\n",
    "from catboost import CatBoostClassifier\n",
    "\n",
    "\n",
    "import optuna\n",
    "from optuna.samplers import TPESampler\n",
    "from optuna import Trial\n",
    "\n",
    "# from google.colab import drive  # GoodleDrive mount \n",
    "# drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6d28a6c9",
   "metadata": {
    "id": "6d28a6c9"
   },
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('C://Users//user//OneDrive//open//train.csv')\n",
    "test_df = pd.read_csv('C:/Users/user/OneDrive/open/test.csv')\n",
    "submit = pd.read_csv('C:/Users/user/OneDrive/open/sample_submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "kLBoulM6u2Fb",
   "metadata": {
    "id": "kLBoulM6u2Fb"
   },
   "outputs": [],
   "source": [
    "# train_df = pd.read_csv('/content/drive/MyDrive/train.csv')\n",
    "# test_df = pd.read_csv('/content/drive/MyDrive/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f75f9df6",
   "metadata": {
    "id": "f75f9df6"
   },
   "outputs": [],
   "source": [
    "train_x = train_df.drop(columns=['PRODUCT_ID', 'TIMESTAMP', 'Y_Class'])\n",
    "train_y = train_df['Y_Class']\n",
    "\n",
    "test_x = test_df.drop(columns=['PRODUCT_ID', 'TIMESTAMP'])\n",
    "# test_x = test_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wqkW_3EbQxj1",
   "metadata": {
    "id": "wqkW_3EbQxj1"
   },
   "source": [
    "# 1.preprocessing\n",
    "- Label Encoding\n",
    "- Missing values fillna(0) \n",
    "- scaling: StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c66d3bf4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c66d3bf4",
    "outputId": "f4f7912a-e484-4664-884f-eae5d9a8f640"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done.\n"
     ]
    }
   ],
   "source": [
    "# qualitative to quantitative\n",
    "qual_col = ['LINE', 'PRODUCT_CODE']\n",
    "\n",
    "for i in qual_col:\n",
    "    le = LabelEncoder()    # TRY one-hot encoding \n",
    "    le = le.fit(train_x[i])\n",
    "    train_x[i] = le.transform(train_x[i])\n",
    "    \n",
    "    for label in np.unique(test_x[i]): \n",
    "        if label not in le.classes_: \n",
    "            le.classes_ = np.append(le.classes_, label)\n",
    "    test_x[i] = le.transform(test_x[i]) \n",
    "print('Done.')  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5dc3e419",
   "metadata": {
    "id": "5dc3e419"
   },
   "outputs": [],
   "source": [
    "# Missing Values \n",
    "train_x = train_x.fillna(0)\n",
    "test_x = test_x.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f42862a2",
   "metadata": {
    "id": "f42862a2"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "stdsc = StandardScaler()\n",
    "\n",
    "X_train_std = stdsc.fit_transform(train_x)\n",
    "X_test_std = stdsc.transform(test_x)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "fa987a0c",
   "metadata": {
    "id": "fa987a0c",
    "outputId": "81c4e49c-0e47-4c2a-efbd-1116ef13f72e"
   },
   "outputs": [],
   "source": [
    "# X_train_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1a75269c",
   "metadata": {
    "id": "1a75269c"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_train_std, train_y,\n",
    "                                                   test_size=0.3,\n",
    "                                                   random_state=37)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "HgRXZthuQD4I",
   "metadata": {
    "id": "HgRXZthuQD4I"
   },
   "source": [
    "# 2.Sequential Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "83ee1fde",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "83ee1fde",
    "outputId": "629fd41c-550a-4f69-ba90-9a5944861a3a",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_10 (Dense)            (None, 512)               1474048   \n",
      "                                                                 \n",
      " dense_11 (Dense)            (None, 3)                 1539      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,475,587\n",
      "Trainable params: 1,475,587\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential([\n",
    "          Dense(512, activation='relu',input_shape=(2878,)),\n",
    "          Dense(3, activation='softmax')])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "52b8e615",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "52b8e615",
    "outputId": "48d50e19-36a9-42c5-8837-0a6214f1402f",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "6/6 [==============================] - 1s 53ms/step - loss: 4.3302 - accuracy: 0.5126 - val_loss: 3.4874 - val_accuracy: 0.5583\n",
      "Epoch 2/10\n",
      "6/6 [==============================] - 0s 37ms/step - loss: 2.5803 - accuracy: 0.5628 - val_loss: 3.9042 - val_accuracy: 0.6667\n",
      "Epoch 3/10\n",
      "6/6 [==============================] - 0s 38ms/step - loss: 2.7223 - accuracy: 0.6904 - val_loss: 3.2307 - val_accuracy: 0.6833\n",
      "Epoch 4/10\n",
      "6/6 [==============================] - 0s 34ms/step - loss: 1.6359 - accuracy: 0.6485 - val_loss: 2.7764 - val_accuracy: 0.6167\n",
      "Epoch 5/10\n",
      "6/6 [==============================] - 0s 30ms/step - loss: 1.4885 - accuracy: 0.7385 - val_loss: 1.5380 - val_accuracy: 0.6750\n",
      "Epoch 6/10\n",
      "6/6 [==============================] - 0s 31ms/step - loss: 0.9898 - accuracy: 0.6611 - val_loss: 2.0354 - val_accuracy: 0.6167\n",
      "Epoch 7/10\n",
      "6/6 [==============================] - 0s 42ms/step - loss: 1.2626 - accuracy: 0.7155 - val_loss: 1.7675 - val_accuracy: 0.6917\n",
      "Epoch 8/10\n",
      "6/6 [==============================] - 0s 39ms/step - loss: 0.8540 - accuracy: 0.7343 - val_loss: 1.8311 - val_accuracy: 0.6250\n",
      "Epoch 9/10\n",
      "6/6 [==============================] - 0s 41ms/step - loss: 0.8310 - accuracy: 0.7615 - val_loss: 1.7061 - val_accuracy: 0.6917\n",
      "Epoch 10/10\n",
      "6/6 [==============================] - 0s 28ms/step - loss: 0.8826 - accuracy: 0.7887 - val_loss: 1.6356 - val_accuracy: 0.6250\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='adam',\n",
    "                   loss='sparse_categorical_crossentropy',\n",
    "                   metrics=['accuracy'])\n",
    "     \n",
    "# X_train = pd.DataFrame(X_train).drop(columns=['X_1'])\n",
    "history = model.fit(X_train_std,\n",
    "                    train_y,\n",
    "                    epochs=10,\n",
    "                    batch_size=80,\n",
    "                    validation_split=0.2) \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "f5a8c473",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "f5a8c473",
    "outputId": "a5e063c6-cdda-48a7-97e8-c651f791725a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZMGAAQ4cOLXSu7777jlGjRvHss89ywAEHMHLkSG699VYuvPBCAFq0aMGCBQu45ZZbuPHGG7n99ttLfH9xT1ed7BLBTcClwLYSXm8NfJLwfHm0rRAzG2Nm+WaWv3LlyioPUkTSX2L1UGK10EMPPUT37t3Jyclh8eLFhapxinrxxRc56aSTaNiwIU2bNmXgwIHbX3v77bc56qij6NKlC3l5eSxevLjUeN577z06dOjAAQccAMBpp53G3Llzt78+ZMgQAA499NDtE9WV5KWXXmLEiBFA8dNVT5kyhdWrV1OnTh169OjBXXfdxaRJk3jrrbdo0qRJqecuj6SVCMxsAPClu883s74l7VbMtp0mP3L3qcBUCHMNVVWMIrLrSvvlnkyDBw9m/PjxLFiwgI0bN9K9e3c++ugjbrzxRubNm8cee+zBqFGjSpx+uoBZcV87YcWzmTNn0q1bN+6++27mzJlT6nnKmqetYCrrkqa6LutcBdNVn3DCCcyaNYtevXoxe/bs7dNVP/HEE4wYMYJLLrmEkSNHlnr+siSzRNAbGGhmS4HpwDFmdl+RfZYDbRKeZwGfJTEmEammGjduTN++fRk9evT20sDatWtp1KgRzZo144svvuDJJ58s9Rx9+vThkUceYePGjaxbt47HHnts+2vr1q2jVatWbN68efvU0QBNmjRh3bp1O53roIMOYunSpbz//vsA/P3vf+eHP/xhhd5b3NNVJ61E4O5XAFcARCWCX7n7L4rs9ijwSzObDhwGrFH7gIiUZNiwYQwZMmR7FVG3bt3IycmhU6dO7LfffvTu3bvU47t3784pp5xCdnY27dq146ijjtr+2m9+8xsOO+ww2rVrR5cuXbZ/+Z966qmcddZZTJkyZXsjMUD9+vW56667OPnkk9myZQs9evRg7NixFXpfkyZN4vTTT6dr1640bNiw0HTVzz//PLVr1+aQQw6hX79+TJ8+nT/84Q/UrVuXxo0bV8kCNimZhjohEQwws7EA7n6bhTLaX4HjgQ3A6e5e6hzT1X0a6g8/hHfegTTpPixSLpqGunrZ1WmoUzL7qLvPAeZEj29L2O7AuamIIW7uYXGO8ePDQt433wyljIUREUkZTUOdAp9/DmeeCU88AcceC3Xrwi9/CfvuC4MHxx2diGQ6TTGRZDNnQpcu8OyzMGUKPP00/OMf0KMHDBsGL78cd4Qi5VPdVjPMVBX5OykRJMnatTB6NJx0ErRtC/Pnw3nnQa1a0KgRPP44ZGXBiSdCwuBGkbRUv359Vq1apWSQ5tydVatWUb9+/V06TlVDSfDSSzBiBHz8MUyYANdcA7vtVnifli3hqafgiCPg+ONDyaBVq3jiFSlLVlYWy5cvRwM601/9+vXJysrapWOUCKrQpk0wcSL87nfQoQPMnQul9Wb7wQ9Cu0HfvnDCCfDCC1AFgwRFqlzdunXp0KFD3GFIkqhqqIosXgyHHQY33ABnnBHmYimjSzMAubmhzWDRIhg6NCQTEZFUUiKopG3bwpD7Qw+FTz+Ff/0Lpk3btV/2/fqFrqXPPANnnRW6moqIpIqqhirhk09g1Ch47rnQ6DttGuy9d8XONXo0LF8eqpaysmDy5CoNVUSkREoEFfTAA2FA2ObNIQGccQaUMJdVuV19dUgG118fksG4cVUTq4hIaZQIdtE334QEMH06HH44/P3vodG3KpjBLbfAihVhwFmrVhpwJiLJpzaCXTB7dhgc9vDDcN11oVdQVSWBAnXqhCSTm6sBZyKSGkoE5bBxY1iV6bjjQiPwq6+G8QF1klSe0oAzEUklJYIyLFgQegT95S9hZHDB82QrGHBWp04YcPb558m/pohkJiWCEmzdGhptDzsM1qwJcwRNmQINGqQuhoIBZytXQv/+UMzaGCIilaZEUIwPP4Q+fUL1z5Ah8NZb8OMfxxNL0QFnmzfHE4eI1FxKBAnc4Y47oFu3MFI4Ly803O65Z7xxJQ44O/NMDTgTkaql7qORL7+EMWPCyOCjj4a77w6zhqYLDTgTkWRRIgAeeyz80l6zBv70J7jggjBddLrRgDMRSYaMTgTr14elI6dNC9VBzz4LnTvHHVXJNOBMRJIhab97zay+mb1uZm+a2WIz+3Ux+/Q1szVmtjC6XZOseIp65RXIzobbb4fLLoPXXkvvJFBAA85EpKolswLke+AYd+8GZAPHm1mvYvZ70d2zo9u1SYwHCL1urr4ajjwydBF94YUwdXS9esm+ctXRgDMRqUpJSwQerI+e1o1usfZ3WbIkzA903XUwciS8+SYcdVScEVVcwYCz2rU14ExEKiepTaJmVtvMFgJfAv9299eK2e3wqProSTPrVMJ5xphZvpnlV3SpvH/9C3JyYOlSmDED7roLmjat0KnSRsGAsy+/1IAzEam4pCYCd9/q7tlAFtDTzIrWwi8A2kXVR/8HzCzhPFPdPdfdc1u2bFmhWLp3h0GD4O23wyCxmqJHDw04E5HKSUknSXdfDcwBji+yfW1B9ZG7zwLqmlmLZMTQpk1oZN1nn2ScPV79+2vAmYhUXDJ7DbU0s92jxw2AY4ElRfbZxyws52JmPaN4ViUrppps9Gj49a/h3nvhqqvijkZEqpNkjiNoBdxjZrUJX/APufvjZjYWwN1vA4YC48xsC7ARONVdv2crSgPORKQirLp97+bm5np+fn7cYaStLVvgpJNg1iz45z9Du4iIiJnNd/fc4l5Lw4kUpDISB5ydemoYOCciUholghooccDZgAEacCYipVMiqKE04ExEykuJoAbTgDMRKQ8lghpOA85EpCxKBBlAA85EpDQZvR5BJklc4axNmzDxnogIKBFklIIBZ5Mnhx5FY8fGHZGIpAMlggxSsMLZZ5/BuefC3nuHwWciktnURpBh6tSBBx8MjchDh8JvfgPbtsUdlYjESYkgAzVqBLNnw89/DtdcA/36QQWXeRCRGkCJIEM1bhxmKp06NSzXmZMD//lP3FGJSByUCDKYGZx1Frz6KjRoAD/8Idx4o7qXimQaJQIhOxvy82HwYLjkknD/zTcxByUiKaNEIAA0axZGIP/lL/Dkk2FpT832LZIZlAhkOzM4/3x48cXQk6h3b7j5ZlUVidR0SgSyk8MOgzfegOOOg1/+MqxrsHZt3FGJSLIoEUix9twTHn0Ufvc7mDEjLHSzaFHcUYlIMigRSIlq1YJLL4XnnoP160NJ4c47445KRKpa0hKBmdU3s9fN7E0zW2xmvy5mHzOzKWb2vpktMrPuyYpHKq5PH1i4MLQZnHEGjBoF334bd1QiUlWSWSL4HjjG3bsB2cDxZtaryD79gI7RbQxwaxLjkUrYay94+ukwe+m994bSwZIlcUclIlUhaYnAg/XR07rRrWj/k0HAvdG+rwK7m1mrZMUklVO7NkyaFBLCl1+GdoP77487KhGprKS2EZhZbTNbCHwJ/NvdXyuyS2vgk4Tny6NtRc8zxszyzSx/pSbFid1xx4VeRTk5MHw4jBsH330Xd1QiUlFJTQTuvtXds4EsoKeZdS6yixV3WDHnmeruue6e27JlyyREKruqdWt4/nm47DK47bbQfvDhh3FHJSIVkZJeQ+6+GpgDHF/kpeVAm4TnWcBnqYhJKq9OHbjhhtDN9KOPwmjkRx6JOyoR2VXJ7DXU0sx2jx43AI4FijYvPgqMjHoP9QLWuPuKZMUkyXHiibBgARxwAAwZAuPHw6ZNcUclIuWVzBJBK+B5M1sEzCO0ETxuZmPNrGCRxFnAh8D7wDTgnCTGI0nUvj289BKcdx78+c9hJtOPP447KhEpD/NqNpFMbm6u52s2tLT2j3+E8QZ168J994WFb0QkXmY2391zi3tNI4ulyp18MsyfD23aQP/+MGECbNkSd1QiUhIlAkmKjh3hlVfgzDPh+utDl9MVav0RSUtKBJI0DRrAtGlwzz3w+uth3MHzz8cdlYgUpUQgSTdyZEgEe+4Jxx4L110X1jsQkfSgRCAp0alTSAbDhsHVV4e2Aw0SF0kPSgSSMo0bw9//Dn/7G8yZE6qKXn457qhERIlAUsoMxowJDcn164fxBpq4TiReSgQZKC8vDACrVSvc5+WlPoacnNDFNDc3jETeuDH1MYhIoESQYfLywi/yZcvCovTLloXncSSDZs3CUphffKGVz0TipJHFGaZ9+/DlX1S7drB0aaqjCY46KsT0/vuw227xxCBS01V6ZLGZNTKzWtHjA8xsoJnVrcogJTVKmv8nznmBrroKPvkkNCSLSOqVt2poLlDfzFoDzwKnA3cnKyhJnrZtd217Kvz4x6Gt4Le/1VQUInEobyIwd98ADAH+z91PAg5JXliSLJMnQ8OGhbc1bBi2x8UslAo++AAeeii+OEQyVbkTgZkdDgwHnoi21UlOSJJMw4fD1KmhTcAs3E+dGrbH6cQToXPnkJA06lgktcqbCC4ErgAecffFZrYfoFljqqnhw0PD8LZt4T7uJAChK+uECfDOOzBzZtzRiGSWciUCd3/B3Qe6+++iRuOv3P38JMdW46RD//10dvLJYdbS664LXVtFJDXK22vofjNramaNgHeA98zskuSGVrOkU//9dFW7Nlx5JbzxBjz5ZNzRiGSO8lYNHeLua4HBhOUl2wIjkhVUTTRhAmzYUHjbhg1hu+wwfHhot1CpQCR1ypsI6kbjBgYD/3L3zYD+m+6CdOy/n47q1oXLLgtzEc2ZE3c0IpmhvIngb8BSoBEw18zaAWtLO8DM2pjZ82b2rpktNrMLitmnr5mtMbOF0e2aXX0D1UU69t9PV6efDq1ahVKBiCRfeRuLp7h7a3fv78Ey4OgyDtsCXOzuBwO9gHPNrLixBy+6e3Z0u3bXwq8+0rH/frqqXx8uuQSeey6UDEQkucrbWNzMzP5kZvnR7Y+E0kGJ3H2Fuy+IHq8D3gVaVzriaipd+++nqzFjoEULJUqRVChv1dCdwDrgZ9FtLXBXeS9iZu2BHOC1Yl4+3MzeNLMnzaxTec9ZHaVj//101agRXHQRPPFE6EUkIslTrtlHzWyhu2eXta2EYxsDLwCT3f2fRV5rCmxz9/Vm1h/4i7t3LOYcY4AxAG3btj10WXHTZ0qNs2ZNKDkdeyw8/HDc0YhUb5WefRTYaGZHJpywN1DmUiJRT6MZQF7RJADg7mvdfX30eBahd1KLYvab6u657p7bsmXLcoYs1V2zZnD++TBjRhhxLCLJUd5EMBa42cyWmtlS4K/A2aUdYGYG3AG86+5/KmGffaL9MLOeUTyryhmTZIALLgjVRL/9bdyRiNRc5e019Ka7dwO6Al3dPQc4pozDehMGnR2T0D20v5mNNbOx0T5DgbfN7E1gCnCqV7eVciSpmjeHcePCusYffBB3NCI1U4VXKDOzj9095b3gtUJZ5vn88zA304gRMG1a3NGIVE9V0UZQ7HkrcaxIue2zD5x1FtxzT1jJTESqVmUSgapwJGUuuSTMPfSHP8QdiUjNU2oiMLN1Zra2mNs6YN8UxShC27Zw2mmhaujzz+OORqRmKTURuHsTd29azK2Ju2uFMkmpyy+HTZvgT8X2QRORiqpM1ZBISu2/PwwbBrfcAqvUyVikyigRSLVyxRXw7bcwZUrckYjUHEoEUq106gRDhoREsGZN3NGI1AxKBFLtTJgAq1eHKiIRqTwlAql2uneHfv1Co/G338YdjUj1p0Qg1dJVV8FXX2mksUhVUCKQaumII+Doo8MAs+++izsakepNiUCqrauugs8+g7vvjjsSkepNiUCqraOPhl694IYbYPPmuKMRqb6UCKTaMgulgmXLIC8v7mhEqi8lAqnW+veH7OywcM3WrXFHI1I9KRFItVZQKvjvf7WusUhFKRFItXfSSXDwwXDddbBtW9zRiFQ/SgRS7dWqBVdeCW+/DY89Fnc0ItWPEoHUCKeeCvvtB5MnhwVsRKT8lAikRqhTJ8xMOm8e/PvfcUcjUr0kLRGYWRsze97M3jWzxWZ2QTH7mJlNMbP3zWyRmXVPVjxS840cCVlZoa2gOnr6aTj0UJg4Eb74Iu5oJJMks0SwBbjY3Q8GegHnmtkhRfbpB3SMbmOAW5MYj9Rwu+0Gl14KL74Ic+fGHU35ucNf/hK6wn76KVx7LbRrB2eeCYsXxx2dZIKkJQJ3X+HuC6LH64B3gdZFdhsE3OvBq8DuZtYqWTFJzXfmmbDXXqGtoDrYtAnOPhsuvBAGDoT334clS2DUqDBIrnPnMNPq7Nlq+5DkSUkbgZm1B3KA14q81Br4JOH5cnZOFpjZGDPLN7P8lStXJi1Oqf4aNIBf/QqeeQZefz3uaEq3ahX8+MdhBtUrr4QZM6BxYzjwQLjtNvjkk1A6WLAAjjsuDJy75x74/vu4I5eaJumJwMwaAzOAC919bdGXizlkp9897j7V3XPdPbdly5bJCFNqkLFjYY890rtUsHgx9OwJr74K990XYq1V5H9jixZw9dVhCo077ggjp0eNgg4d4Prr4euvYwldaqCkJgIzq0tIAnnu/s9idlkOtEl4ngV8lsyYpOZr0iRUtTz6KCxaFHc0O3viCTj8cNiwAV54AYYPL33/+vVh9Gh46y146qlQXTRhArRpA+eeC//7X2rilpormb2GDLgDeNfd/1TCbo8CI6PeQ72ANe6+IlkxSeY477yQEK6/Pu5IdnCHP/4RTjwR9t8/VF0ddlj5jzeDn/wkVHstWgQ/+1moVjrwQBg8ODSSqx1BKiKZJYLewAjgGDNbGN36m9lYMxsb7TML+BB4H5gGnJPEeCSD7LEH/PKX8NBD8N57cUcT6vXPOCO0XwwZEr6027Qp+7iSdOkCd90Vqo2uvDKcr0+fkFimT4ctW6oudqn5zKvZT4jc3FzPz8+POwypBlauDN0wf/azeBevWbkyfPm/9FKo8580aef2gMrasCE0JP/5z6GqqG1buOCCkHyaNavaa0n1ZGbz3T23uNc0slhqrJYtQ9fM++6Djz6KJ4a33oIePSA/P/xSv/baqk8CAA0bwrhxoevpv/4F7dvDxReHUsfFF4eSg0hJlAikRvvVr6B2bfj971N/7cceC2srb9oUBridckryr1mrVhiP8MILYbqNAQPCYLUf/CDMx5TuXWolHkoEUqO1bh163Nx5Zxi1mwruIfEMGgQHHRS+kHv0SM21E+Xmwv33h9LQRRfBk0+GNoSjjoKZM9N3IZ+1a+Hdd8OcUbNnq5tsKqiNQGq8jz6Cjh1DT6I//zm51/ruu1Adde+9oQRw552h2iYdrF0b4rnpplBVtP/+oZvtqFHQqFHyr791K3z5JSxfHpJy4i1x2/r1Ox+7//4hmfbsGe5zctLnc60uSmsjUCKQjDBqVOhBtHRpmIIiGb74IiyS88oroS3gqqtCl890s2ULPPJI6Mr62muhh9XYsaGX1b77VuycGzaU/uX+6aewYsXOpZA6dcI1W7fe+ZaVFarV8vNDlda8eeGcEKr7OncunBw6dw7nk+IpEUjGe++9sIrZ5ZcnZ2zBwoWhbv6rr0JpYOjQqr9GVXMPSeuPfwyJoU4dGDYMxo+Hbt127PPVV6V/wX/6KXzzzc7nb9q0+C/3xOd77bVrjecrVoSEMG/ejuRQcO0GDUJJoSAx9OwZ2kbSMRnHQYlAhNBYOmtWqBbZY4+qO+8jj8AvfhHO+eij0L0aTqb+wQehUfnOO+Hbb6FrV1i3LnzJb9pUeF8z2Gefkr/cC25NmiQ/bvcQe2JiWLAANm4Mr++xR2grSUwOrTJ0WkslAhHCaNxu3eDXv4Zrrqn8+dzht78N0z307BkaYKv7l8w338DUqfDss+HXenG/5vfZJ72rYLZsCXM5JSaHt97aUS3VunXhKqXcXNh991hDTgklApHIoEFhFO6yZZX7xbpxY5jy+v774ec/h9tvD1UTkp42bAjVdwWJYd68wnM0HXBA4eSQnV3z/p5KBCKRgvl9fv97uOSSip1jxYowt8/rr4f2hssvVz10dfTNN4Ubol9/PfxtIZR4unQp3BDdti3svXdyBgSmghKBSIKf/CT8Oly6dNd/9S1YEEoV33wTRiwPHpyEACU2n35auNQwbx6sWbPj9bp1w2jtdu1CYki8tWsXXkvXbq1KBJKW8vJC/frHH4f/SJMnlz0lc1WYOxd++EOYMiWMLSivhx8O6yK3aBFGDRf0rJGaa9u2sGrcf/8bqhM//rjw7bPPwj6JWrQonByKJoxd7SlVVZQIJO3k5cGYMaHutkDDhqGhMhXJoE+fMNDs/fehXr3S93WH3/wmLCp/+OGhl9Deeyc/Rkl/mzeHZFBckvj447C96AC5evVCyaFogkgsVSSjfUKJQNJO+/bFT4TWrl2oskm2Z54JVURTp8JZZ5W834YNcPrpYTDayJFh/7ISh0gBd1i9uuQkUVCqKPo13LLlzgmibdtQCt1//4rFokQgaadWreIXUTHbuaidDO6h0XjVqjDYrLjukJ9+GtoA5s+HG24IjctqFJaqtnlz+LdWNEEkPv/227DvZZeFf4sVUVoiSOPewFKTtW1bfImgbdvUXN8stE8MHhymh/7FLwq/Pm9eaBRety6MDxg4MDVxSeapWzeUkNu3L/71xFJFstaWqKYdoaS6mzx5594VDRumdsH5E08MXQQnTy5cCnnwwdCGUK8evPyykoDEyyyMkO7WreRkUVlKBBKL4cNDfXu7duEfert2qWsoLlCrVigVLFkC//xnSAbXXBOmosjNDd0Iu3RJXTwicVEbgWS0rVvhkENCL43994cZM8L6BbfeCrvtFnd0IlUnlqUqzexOM/vSzN4u4fW+ZrYmYWH7Kpj9RWTX1K4NV1wBb765Y2rm229XEpDMksyqobuB48vY50V3z45u1yYxFpESmYV5h7ZtC4PM7r8/7ohEUitpicDd5wJaZE7SWl4enHNO6B0EoSfTmDFhu0imiLux+HAze9PMnjSzTiXtZGZjzCzfzPJXrlyZyvikhpswofDoZgjPJ0yIJx6ROMSZCBYA7dy9G/B/wMySdnT3qe6e6+65LVu2TFV8kgE+/njXtovURLElAndf6+7ro8ezgLpm1iKueCQzlTSALVUD20TSQWyJwMz2MQsD9s2sZxTLqrjikcyUDgPbROKWtCkmzOwBoC/QwsyWAxOBugDufhswFBhnZluAjcCpXt0GNUi1VzCALY7psEXShQaUiYhkgFgGlImISPWgRCAikuGUCEREMpwSgYhIhlMiEBHJcEoEIrJdXl5Y/KRWrXCvOZcyg5aqFBEgfOmPGbNj7qWCCfhA4ypqOpUIRATQBHyZTIlARABNwJfJlAhEBNAEfJlMiUBEAE3Al8mUCEQECA3CU6dCu3Zh+c527cJzNRTXfOo1JCLbDR+uL/5MpBKBiEiGUyIQEclwSgQiIhlOiUBEJMMpEYiIZDglAhGRDJe0RGBmd5rZl2b2dgmvm5lNMbP3zWyRmXVPViwiIlKyZJYI7gaOL+X1fkDH6DYGuDWJsYikPU0BLXFJWiJw97nA16XsMgi414NXgd3NrFWy4hFJZwVTQC9bBu47poBWMpBUiLONoDXwScLz5dG2nZjZGDPLN7P8lStXpiQ4kVTSFNASpzgTgRWzzYvb0d2nunuuu+e2bNkyyWGJpJ6mgJY4xZkIlgNtEp5nAZ/FFItIrDQFtMQpzkTwKDAy6j3UC1jj7itijEckNpoCWuKUzO6jDwCvAAea2XIzO8PMxprZ2GiXWcCHwPvANOCcZMUiku40BbSUJtk9ysy92Gr5tJWbm+v5+flxhyEikhIFPcoSOxM0bLjrPxTMbL675xb3mkYWi0ja0ZiKHVLRo0yJQETSSjqNqUiHhJSKHmVKBCKSVtJlTEW6JKRU9ChTIhCRtJIuYyrSJSGlokeZEoGIpJV0GVORLgkpFT3KlAhEJK2ky5iKdElIEL70ly6FbdvCfVV3K1YiEJG0ki5jKtIlIaVCnbgDEBEpavjw+AfTFVx/woRQHdS2bUgCcceVDEoEIiIlSIeElAqqGhIRyXBKBCIiGU6JQEQkwykRiIhkOCUCEZEMV+2moTazlcCyuOOopBbAV3EHkUb0eRSmz2MHfRaFVebzaOfuxa71W+0SQU1gZvklzQueifR5FKbPYwd9FoUl6/NQ1ZCISIZTIhARyXBKBPGYGncAaUafR2H6PHbQZ1FYUj4PtRGIiGQ4lQhERDKcEoGISIZTIkghM2tjZs+b2btmttjMLog7priZWW0ze8PMHo87lriZ2e5m9rCZLYn+jRwed0xxMrOLov8nb5vZA2ZWP+6YUsnM7jSzL83s7YRte5rZv83sf9H9HlVxLSWC1NoCXOzuBwO9gHPN7JCYY4rbBcC7cQeRJv4CPOXuBwHdyODPxcxaA+cDue7eGagNnBpvVCl3N3B8kW2XA8+6e0fg2eh5pSkRpJC7r3D3BdHjdYT/6K3jjSo+ZpYFnADcHncscTOzpkAf4A4Ad9/k7qtjDSp+dYAGZlYHaAh8FnM8KeXuc4Gvi2weBNwTPb4HGFwV11IiiImZtQdygNdiDiVONwGXAttijiMd7AesBO6KqspuN7NGcQcVF3f/FLgR+BhYAaxx92fijSot7O3uKyD8sAT2qoqTKhHEwMwaAzOAC919bdzxxMHMBgBfuvv8uGNJE3WA7sCt7p4DfEsVFfuro6juexDQAdgXaGRmv4g3qppLiSDFzKwuIQnkufs/444nRr2BgWa2FJgOHGNm98UbUqyWA8vdvaCE+DAhMWSqY4GP3H2lu28G/gkcEXNM6eALM2sFEN1/WRUnVSJIITMzQh3wu+7+p7jjiZO7X+HuWe7entAI+Jy7Z+wvPnf/HPjEzA6MNv0IeCfGkOL2MdDLzBpG/29+RAY3nid4FDgtenwa8K+qOKkWr0+t3sAI4C0zWxhtu9LdZ8UXkqSR84A8M9sN+BA4PeZ4YuPur5nZw8ACQm+7N8iw6SbM7AGgL9DCzJYDE4EbgIfM7AxCsjy5Sq6lKSZERDKbqoZERDKcEoGISIZTIhARyXBKBCIiGU6JQEQkwykRiETMbKuZLUy4VdnIXjNrnziLpEg60TgCkR02unt23EGIpJpKBCJlMLOlZvY7M3s9uu0fbW9nZs+a2aLovm20fW8ze8TM3oxuBVMj1DazadEc+8+YWYNo//PN7J3oPNNjepuSwZQIRHZoUKRq6JSE19a6e0/gr4RZU4ke3+vuXYE8YEq0fQrwgrt3I8wXtDja3hG42d07AauBn0bbLwdyovOMTc5bEymZRhaLRMxsvbs3Lmb7UuAYd/8wmjTwc3dvbmZfAa3cfXO0fYW7tzCzlUCWu3+fcI72wL+jBUUws8uAuu5+nZk9BawHZgIz3X19kt+qSCEqEYiUj5fwuKR9ivN9wuOt7GijOwG4GTgUmB8txCKSMkoEIuVzSsL9K9Hjl9mxfOJw4KXo8bPAONi+JnPTkk5qZrWANu7+PGGRnt2BnUolIsmkXx4iOzRImBUWwvrBBV1I65nZa4QfT8OibecDd5rZJYTVxQpmC70AmBrNELmVkBRWlHDN2sB9ZtYMMODPWqJSUk1tBCJliNoIct39q7hjEUkGVQ2JiGQ4lQhERDKcSgQiIhlOiUBEJMMpEYiIZDglAhGRDKdEICKS4f4fF9mfpgywb4QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = history.history[\"loss\"]\n",
    "val_loss = history.history[\"val_loss\"]\n",
    "epochs = range(1, len(loss) + 1)\n",
    "plt.plot(epochs, loss, \"bo\", label=\"Training loss\")\n",
    "plt.plot(epochs, val_loss, \"b\", label=\"Validation loss\")\n",
    "plt.title(\"Training and validation loss\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()   \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "026847cf",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "026847cf",
    "outputId": "da089c2a-9665-4fb3-9b90-8496f4f4ab2c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "acc = history.history[\"accuracy\"]\n",
    "val_acc = history.history[\"val_accuracy\"]\n",
    "plt.plot(epochs, acc, \"bo\", label=\"Training accuracy\")\n",
    "plt.plot(epochs, val_acc, \"b\", label=\"Validation accuracy\")\n",
    "plt.title(\"Training and validation accuracy\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()\n",
    "plt.show() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "27e457a9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "27e457a9",
    "outputId": "931587c8-b6eb-4029-a962-123b309a381d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.85592158, -0.71324153, -1.17783182, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 0.18187866,  0.46174038,  0.85784283, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-0.46119267, -1.88822344, -1.17783182, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       ...,\n",
       "       [-0.67347484,  0.46174038, -0.1599945 , ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-0.34120702,  1.04923134,  0.85784283, ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [-0.54919633, -1.30073248, -1.17783182, ...,  0.        ,\n",
       "         0.        ,  0.        ]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "2fab4505",
   "metadata": {
    "id": "2fab4505"
   },
   "outputs": [],
   "source": [
    "X_test_std = stdsc.transform(test_x)    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2afZOBbYFMpy",
   "metadata": {
    "id": "2afZOBbYFMpy"
   },
   "outputs": [],
   "source": [
    "\n",
    "# a = pd.DataFrame(test_x.iloc[:,-1:])\n",
    "# test_x = pd.concat([test_x ,a ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "57a8de60",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "57a8de60",
    "outputId": "aac13e33-4b10-44bc-b2f2-bcaf4f881598",
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "in user code:\n\n    File \"C:\\Users\\user\\.anaconda\\navigator\\logs\\anaconda(CAU) install\\lib\\site-packages\\keras\\engine\\training.py\", line 2137, in predict_function  *\n        return step_function(self, iterator)\n    File \"C:\\Users\\user\\.anaconda\\navigator\\logs\\anaconda(CAU) install\\lib\\site-packages\\keras\\engine\\training.py\", line 2123, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"C:\\Users\\user\\.anaconda\\navigator\\logs\\anaconda(CAU) install\\lib\\site-packages\\keras\\engine\\training.py\", line 2111, in run_step  **\n        outputs = model.predict_step(data)\n    File \"C:\\Users\\user\\.anaconda\\navigator\\logs\\anaconda(CAU) install\\lib\\site-packages\\keras\\engine\\training.py\", line 2079, in predict_step\n        return self(x, training=False)\n    File \"C:\\Users\\user\\.anaconda\\navigator\\logs\\anaconda(CAU) install\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"C:\\Users\\user\\.anaconda\\navigator\\logs\\anaconda(CAU) install\\lib\\site-packages\\keras\\engine\\input_spec.py\", line 295, in assert_input_compatibility\n        raise ValueError(\n\n    ValueError: Input 0 of layer \"sequential_5\" is incompatible with the layer: expected shape=(None, 2878), found shape=(None, 2877)\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_20532/1788366406.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpreds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test_std\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mpreds\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.anaconda\\navigator\\logs\\anaconda(CAU) install\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     68\u001b[0m             \u001b[1;31m# To get the full stack trace, call:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     69\u001b[0m             \u001b[1;31m# `tf.debugging.disable_traceback_filtering()`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 70\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     71\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     72\u001b[0m             \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\.anaconda\\navigator\\logs\\anaconda(CAU) install\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mtf__predict_function\u001b[1;34m(iterator)\u001b[0m\n\u001b[0;32m     13\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m                     \u001b[0mdo_return\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m                     \u001b[0mretval_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mag__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconverted_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mag__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mld\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep_function\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mag__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mld\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mag__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mld\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfscope\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m                 \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m                     \u001b[0mdo_return\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: in user code:\n\n    File \"C:\\Users\\user\\.anaconda\\navigator\\logs\\anaconda(CAU) install\\lib\\site-packages\\keras\\engine\\training.py\", line 2137, in predict_function  *\n        return step_function(self, iterator)\n    File \"C:\\Users\\user\\.anaconda\\navigator\\logs\\anaconda(CAU) install\\lib\\site-packages\\keras\\engine\\training.py\", line 2123, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"C:\\Users\\user\\.anaconda\\navigator\\logs\\anaconda(CAU) install\\lib\\site-packages\\keras\\engine\\training.py\", line 2111, in run_step  **\n        outputs = model.predict_step(data)\n    File \"C:\\Users\\user\\.anaconda\\navigator\\logs\\anaconda(CAU) install\\lib\\site-packages\\keras\\engine\\training.py\", line 2079, in predict_step\n        return self(x, training=False)\n    File \"C:\\Users\\user\\.anaconda\\navigator\\logs\\anaconda(CAU) install\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"C:\\Users\\user\\.anaconda\\navigator\\logs\\anaconda(CAU) install\\lib\\site-packages\\keras\\engine\\input_spec.py\", line 295, in assert_input_compatibility\n        raise ValueError(\n\n    ValueError: Input 0 of layer \"sequential_5\" is incompatible with the layer: expected shape=(None, 2878), found shape=(None, 2877)\n"
     ]
    }
   ],
   "source": [
    "preds = model.predict(X_test_std)\n",
    "\n",
    "preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "Cyfb21qOt4dn",
   "metadata": {
    "id": "Cyfb21qOt4dn"
   },
   "outputs": [],
   "source": [
    "submit = pd.read_csv('/content/drive/MyDrive/sample_submission.csv')  \n",
    "\n",
    "submit['Y_Class'] = preds \n",
    "submit.to_csv('smartFactory_submission17.csv', index=False)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14993522",
   "metadata": {
    "id": "14993522"
   },
   "outputs": [],
   "source": [
    "submit['Y_Class'] = preds\n",
    "     \n",
    "\n",
    "submit.to_csv('C:/Users/user/OneDrive/open/mid_submission-MLP.csv', index=False)\n",
    "# submit.to_csv('mid_submission-MLP.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "yreBA4JMQPJ9",
   "metadata": {
    "id": "yreBA4JMQPJ9"
   },
   "source": [
    "# 3.catBoost classifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7f4ff7b",
   "metadata": {
    "id": "e7f4ff7b"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test =train_test_split(train_x, train_y,\n",
    "                                                   test_size=0.3,\n",
    "                                                   random_state=37)\n",
    "     \n",
    "\n",
    "def objective(trial):\n",
    "    model = CatBoostClassifier(\n",
    "        iterations=trial.suggest_int(\"iterations\", 100, 1000),\n",
    "        learning_rate=trial.suggest_float(\"learning_rate\", 1e-3, 1e-1, log=True),\n",
    "        depth=trial.suggest_int(\"depth\", 4, 10),\n",
    "        l2_leaf_reg=trial.suggest_float(\"l2_leaf_reg\", 1e-8, 100.0, log=True),\n",
    "        bootstrap_type=trial.suggest_categorical(\"bootstrap_type\", [\"Bayesian\"]),\n",
    "        random_strength=trial.suggest_float(\"random_strength\", 1e-8, 10.0, log=True),\n",
    "        bagging_temperature=trial.suggest_float(\"bagging_temperature\", 0.0, 10.0),\n",
    "        od_type=trial.suggest_categorical(\"od_type\", [\"IncToDec\", \"Iter\"]),\n",
    "        od_wait=trial.suggest_int(\"od_wait\", 10, 50),\n",
    "        verbose=False\n",
    "    )\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    return f1_score(y_test, y_pred, average='macro')\n",
    "     \n",
    "# hyper-parameter tuning with OPTUNA  \n",
    "optuna.logging.set_verbosity(optuna.logging.WARNING)\n",
    "\n",
    "sampler = TPESampler(seed=316)\n",
    "study = optuna.create_study(study_name=\"catboost\", direction=\"maximize\", sampler=sampler)\n",
    "study.optimize(objective, n_trials=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ee90ada",
   "metadata": {
    "id": "4ee90ada"
   },
   "outputs": [],
   "source": [
    "print(\"Number of finished trials: \", len(study.trials))\n",
    "print(\"Best trial:\")\n",
    "trial = study.best_trial\n",
    "print(\"  Value: \", trial.value)\n",
    "print(\"  Params: \",trial.params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b32047d8",
   "metadata": {
    "id": "b32047d8"
   },
   "outputs": [],
   "source": [
    "print(\"Best Score:\", study.best_value)\n",
    "print(\"Best trial:\", study.best_trial.params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e5dd359",
   "metadata": {
    "id": "2e5dd359"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91bbd3b7",
   "metadata": {
    "id": "91bbd3b7"
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(model, open(\"catboost_model.pkl\", \"wb\"))\n",
    "     \n",
    "\n",
    "optuna.visualization.plot_optimization_history(study)\n",
    "     \n",
    "\n",
    "optuna.visualization.plot_parallel_coordinate(study)\n",
    "     \n",
    "\n",
    "# 각 파라미터들의 상관관계\n",
    "optuna.visualization.plot_contour(\n",
    "    study,\n",
    "    params=[\n",
    "        \"iterations\",\n",
    "        \"learning_rate\",\n",
    "        \"depth\",\n",
    "        \"l2_leaf_reg\",\n",
    "        \"bootstrap_type\",\n",
    "        \"random_strength\",\n",
    "        \"bagging_temperature\",\n",
    "        \"od_type\",\n",
    "        \"od_wait\"\n",
    "        ],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fab2a838",
   "metadata": {
    "id": "fab2a838"
   },
   "outputs": [],
   "source": [
    "# 하이퍼파라미터 중요도\n",
    "optuna.visualization.plot_param_importances(study)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e534f3a3",
   "metadata": {
    "id": "e534f3a3"
   },
   "outputs": [],
   "source": [
    "preds = model.predict(test_x)\n",
    "     \n",
    "\n",
    "preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b687c6a1",
   "metadata": {
    "id": "b687c6a1"
   },
   "outputs": [],
   "source": [
    "submit = pd.read_csv('C:/Users/user/OneDrive/open/sample_submission.csv')\n",
    "submit['Y_Class'] = preds\n",
    "\n",
    "submit.to_csv('C:/Users/user/OneDrive/open/smartFactory_submission_MLP.csv', index=False)\n",
    "# submit.to_csv('smartFactory_submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20a97788",
   "metadata": {
    "id": "20a97788"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9c15ce7",
   "metadata": {
    "id": "c9c15ce7",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# BEST OUTPUT \n",
    "# a = pd.read_csv('C:/Users/user/Downloads/smartFactory_submission18.csv')\n",
    "# sns.countplot(x='Y_Class', data=a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "369e67a9",
   "metadata": {
    "id": "369e67a9"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
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  "gpuClass": "standard",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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